Forcing-informed resolvent analysis extracts data-consistent forcing and response modes for self-sustained flows by estimating input-output subspaces from nonlinear forcing snapshots.
Modal Analysis of Fluid Flows: An Overview
10 Pith papers cite this work, alongside 1,844 external citations. Polarity classification is still indexing.
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UNVERDICTED 10representative citing papers
Tensor networks enable tunable, objective compression of 1D fluid data with lossless reconstruction at high bond dimension and efficient in-compressed-space operations like periodic convolution.
A spectral mPOD reformulation uses compact disjoint frequency masks to block-diagonalize the correlation operator, shrinking eigenvalue problems to per-band sizes while recovering identical modes and singular values.
SS-POD augments standard POD-Galerkin with a spectral-subspace partition and local POD to achieve lower out-of-sample error than either plain POD or pure spectral-Galerkin when only a handful of snapshots are available.
Rarefied hypersonic bow shocks over a cylinder inflate via multi-scale compression-relaxation with density becoming nearly rank-one while Mach and thermal fields retain independent modes, as separated by Knudsen and Mach sweeps in DSMC data.
POD output projection plus balanced truncation creates reduced-order models that make LMI control synthesis tractable for minimizing transient energy growth in channel flow, outperforming LQR.
Applies direct-adjoint eigenmode analysis and biorthogonal decomposition to quantify how a reacting base state modifies Kelvin-Helmholtz instability receptivity in a compressible temporal mixing layer.
Hierarchical DMD decomposition of Antarctic sea ice data separates interannual variability from an emerging climate trend and supports two-year forecasts via a regularized predictive model (IceDMD) that outperforms existing approaches.
MoTIF uses HOSVD to separate multi-parametric unsteady flow data into modal components, applies GPR for parametric and spatial interpolation and RNN for temporal forecasting, achieving under 2% relative RMS error on laminar flow cases with varying Reynolds number and angle of attack.
Reviews linear and nonlinear SciML surrogates for coupled fluid flow and transport, with new PINN modeling of turbidity currents and β-VAE mode extraction from Rayleigh-Bénard convection.
citing papers explorer
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Forcing-informed resolvent analysis: Identification of input-output relations in self-sustained flows
Forcing-informed resolvent analysis extracts data-consistent forcing and response modes for self-sustained flows by estimating input-output subspaces from nonlinear forcing snapshots.
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Tensor network compression using fluid dynamics as a testbed: Analytical foundations in one dimension
Tensor networks enable tunable, objective compression of 1D fluid data with lossless reconstruction at high bond dimension and efficient in-compressed-space operations like periodic convolution.
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A Fast Spectral Formulation of the Multiscale Proper Orthogonal Decomposition
A spectral mPOD reformulation uses compact disjoint frequency masks to block-diagonalize the correlation operator, shrinking eigenvalue problems to per-band sizes while recovering identical modes and singular values.
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A spectral-subspace-augmented POD-Galerkin method for parametrized PDEs with limited snapshot data
SS-POD augments standard POD-Galerkin with a spectral-subspace partition and local POD to achieve lower out-of-sample error than either plain POD or pure spectral-Galerkin when only a handful of snapshots are available.
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Rarefaction-induced inflation and similarity breakdown of hypersonic bow shocks over a circular cylinder
Rarefied hypersonic bow shocks over a cylinder inflate via multi-scale compression-relaxation with density becoming nearly rank-one while Mach and thermal fields retain independent modes, as separated by Knudsen and Mach sweeps in DSMC data.
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Control-oriented model reduction for minimizing transient energy growth in shear flows
POD output projection plus balanced truncation creates reduced-order models that make LMI control synthesis tractable for minimizing transient energy growth in channel flow, outperforming LQR.
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Receptivity and Biorthogonal Decomposition in a Reacting Temporal Mixing Layer
Applies direct-adjoint eigenmode analysis and biorthogonal decomposition to quantify how a reacting base state modifies Kelvin-Helmholtz instability receptivity in a compressible temporal mixing layer.
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Multiscale Decomposition Reveals Predictable Interannual Variability and Climate Trends in Antarctic Sea Ice Loss
Hierarchical DMD decomposition of Antarctic sea ice data separates interannual variability from an emerging climate trend and supports two-year forecasts via a regularized predictive model (IceDMD) that outperforms existing approaches.
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MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
MoTIF uses HOSVD to separate multi-parametric unsteady flow data into modal components, applies GPR for parametric and spatial interpolation and RNN for temporal forecasting, achieving under 2% relative RMS error on laminar flow cases with varying Reynolds number and angle of attack.
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Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport
Reviews linear and nonlinear SciML surrogates for coupled fluid flow and transport, with new PINN modeling of turbidity currents and β-VAE mode extraction from Rayleigh-Bénard convection.